Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Clinical validation of a Novel Robotic Device for MR-Guided Prostate Biopsy: Initial Patient Experience.

Journal of medical robotics research·2026
Same author

Sample-Specific Debiasing for Better Image-Text Models.

Proceedings of machine learning research·2026
Same author

AI-assisted cryo-dose estimation and tissue injury modeling for assessing side effects in MRI-guided prostate cryoablation: a retrospective study.

Physics in medicine and biology·2026
Same author

Intraoperative Registration by Cross-Modal Inverse Neural Rendering.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Two Projections Suffice for Cerebral Vascular Reconstruction.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025

Related Experiment Video

Updated: May 14, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Fully automatic 3D segmentation of iceball for image-guided cryoablation.

Xinyang Liu1, Kemal Tuncali, William M Wells

  • 1Department of Radiology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA. xinyang@bwh.harvard.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study introduces a fully automatic method for segmenting cryoablation iceballs in real-time. The novel approach accurately and efficiently identifies the ablated volume, improving tumor coverage assessment during procedures.

More Related Videos

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon
07:52

Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon

Published on: December 1, 2023

Related Experiment Videos

Last Updated: May 14, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon
07:52

Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon

Published on: December 1, 2023

Area of Science:

  • Medical Imaging
  • Interventional Radiology
  • Computational Anatomy

Background:

  • Accurate segmentation of cryoablation iceballs is vital for assessing tumor coverage.
  • Current semi-automatic methods are time-consuming and unsuitable for real-time intraprocedure use.

Purpose of the Study:

  • To develop a fully automatic and efficient method for segmenting cryoablation iceballs from 3D image time series.
  • To improve intraprocedure guidance for interventionalists by providing real-time tumor coverage information.

Main Methods:

  • Utilized a graph cuts segmentation framework.
  • Incorporated time-evolving iceball shape priors modeled from experimental growth parameters.
  • Generated shape prior mask images for each timepoint in the imaging series.

Main Results:

  • The fully automatic approach demonstrated accuracy and robustness in segmentation.
  • Achieved high efficiency compared to manual and semi-automatic segmentation methods.
  • Results were validated against ITK-SNAP for 8 timepoints across 2 cases.

Conclusions:

  • The proposed fully automatic segmentation method is accurate, robust, and efficient for intraprocedure cryoablation.
  • This innovative approach significantly outperforms conventional semi-automatic techniques for real-time iceball segmentation.
  • Enables better determination of tumor coverage by the ablated volume during cryoablation procedures.